In this article, we present our approach to single-modality vision
representation learning. Understanding vision representations of product
content is vital for recommendations, search, and advertising applications in
e-commerce. We detail and contrast techniques used to fine tune large-scale
vision representation learning models in an efficient manner under low-resource
settings, including several pretrained backbone architectures, both in the
convolutional neural network as well as the vision transformer family. We
highlight the challenges for e-commerce applications at-scale and highlight the
efforts to more efficiently train, evaluate, and serve visual representations.
We present ablation studies for several downstream tasks, including our
visually similar ad recommendations. We evaluate the offline performance of the
derived visual representations in downstream tasks. To this end, we present a
novel text-to-image generative offline evaluation method for visually similar
recommendation systems. Finally, we include online results from deployed
machine learning systems in production at Etsy.

本文提出了一种单模态视觉表征学习的方法，主要用于电子商务中的产品推荐、搜索和广告应用，包括预训练骨干架构、卷积神经网络和视觉变换器家族等。通过离线和在线的方式，我们对实验方法进行了评估和分析，并提出了新的文本到图像生成离线评估方法来评估视觉相似度的推荐系统，在 Etsy 的生产环境中进行了机器学习系统的应用。

高效大规模视觉表征学习

Efficient Large-Scale Vision Representation Learning

In e-commerce, product content, especially product images have a significant
influence on a customer's journey from product discovery to evaluation and
finally, purchase decision. Since many e-commerce retailers sell items from
other third-party marketplace sellers besides their own, the content published
by both internal and external content creators needs to be monitored and
enriched, wherever possible. Despite guidelines and warnings, product listings
that contain offensive and non-compliant images continue to enter catalogs.
Offensive and non-compliant content can include a wide range of objects, logos,
and banners conveying violent, sexually explicit, racist, or promotional
messages. Such images can severely damage the customer experience, lead to
legal issues, and erode the company brand. In this paper, we present a computer
vision driven offensive and non-compliant image detection system for extremely
large image datasets. This paper delves into the unique challenges of applying
deep learning to real-world product image data from retail world. We
demonstrate how we resolve a number of technical challenges such as lack of
training data, severe class imbalance, fine-grained class definitions etc.
using a number of practical yet unique technical strategies. Our system
combines state-of-the-art image classification and object detection techniques
with budgeted crowdsourcing to develop a solution customized for a massive,
diverse, and constantly evolving product catalog.

本文介绍了一种计算机视觉的非法和非合规图像检测系统，解决了真实世界产品图像数据应用深度学习所面临的挑战，该系统结合了最先进的图像分类和目标检测技术，并采用定额众包的方法进行开发。